基于大数据平台的电力负荷预测  

Power load prediction based on big data platform

作　　者：刘南艳[1] 贺敏 赵建文 LIU Nanyan;HE Min;ZHAO Jianwen(School of Computer Science and Technology,Xi’an University of Science and Technology,Xi’an 710054,China)

机构地区：[1]西安科技大学计算机科学与技术学院,陕西西安710054

出　　处：《现代电子技术》2018年第20期153-156,共4页Modern Electronics Technique

基　　金：工业科技攻关项目:矿井电网智能漏电保护研发(2015GY049)~~

摘　　要：电力行业是大数据应用的重要领域之一,电力系统每时每刻都在产生大规模、种类繁多的电力数据,面对海量数据,该如何将它们高效的处理和存储,并进行有效开发利用十分关键。因此,研究基于Hadoop云计算平台海量数据下的电力负荷预测方法,并在MapReduce编程框架的基础下,将K-Means算法进行改良和优化。实验结果表明,提出的方法是可行的,数据处理时间大大缩短,算法精度也能满足负荷预测的要求。Electric power industry is one of the most important fields of big data application.The power system is producing a great variety of large-scale electric power data all the time,and how to effectively process,store,develop and utilize the massive data is very important.Therefore,a power load prediction method based on massive data is researched using the Hadoop cloud computing platform.The K-Means algorithm is improved and optimized on the basics of the Mapreduce programming frame-work.The experimental results show that,the proposed method is feasible,has greatly-reduced data processing time,and its algorithm accuracy can meet the load prediction requirement.

关 键 词：大数据 HADOOP 云计算 MAPREDUCE 电力系统 电力负荷预测 

分 类 号：TN915.853-34[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]